Leveraging Retrieval-Augmented Generation (RAG) for FinTech Regulatory Compliance Automation

Authors

  • Ranjeet Kumar Pilot Company, USA Author
  • Pralohith Reddy Chinthalapelly Mayo Clinic, USA Author

Keywords:

Retrieval-Augmented Generation, FAISS, Dense Passage Retrieval, GPT-4, LLaMA-3, regulatory compliance

Abstract

Research using RAG automates FinTech regulatory compliance. Using FAISS and DPR with GPT-4 and LLaMA-3 produces a hybrid architecture for dynamic compliance checks. The program organizes multijurisdictional financial institutions' Basel III, MiFID II, and SEC reporting. Factual accuracy, semantic coherence, hallucination reduction, and interpretability are tested experimentally. Traceable, context-grounded RAG-based systems improve auditability and regulatory transparency over standalone big language models. According to study, RAG's scalable and explainable AI-driven global financial regulatory information may disrupt compliance automation.

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References

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Published

06-03-2024

How to Cite

[1]
Ranjeet Kumar and Pralohith Reddy Chinthalapelly, “Leveraging Retrieval-Augmented Generation (RAG) for FinTech Regulatory Compliance Automation”, American J Auton Syst Robot Eng, vol. 4, pp. 206–237, Mar. 2024, Accessed: Dec. 12, 2025. [Online]. Available: https://ajasre.org/index.php/publication/article/view/82